MH-MuG: Collaborative Music Generation Game Between AI Agents Toward Emergent Musical Creativity

This study explores collaborative music generation using multiple symbolic music generation AI agents, grounding the process in the Systems Model of Creativity. We propose the Metropolis-Hastings Music Generation Game (MH-MuG), which integrates latent diffusion models (LDMs) and formulates the collaboration as a decentralized Bayesian inference process. In MH-MuG, agents with distinct musical knowledge (pre-trained on Classical and Jazz) alternate between composer and listener roles. This interaction functions as a Markov Chain Monte Carlo (MCMC) method, enabling agents to collectively sample from a joint distribution that integrates their knowledge. We compared two variants: one without fine-tuning (w/o f.t.), modeling a fixed-knowledge game, and one with fine-tuning (w/ f.t.), modeling mutual adaptation. Our experiments yielded two key findings: (1) The (w/o f.t.) variant, functioning as a collaborative music generation game, successfully generated high-quality, stylistically fused music. (2) Conversely, the (w/ f.t.) variant led to a significant reduction in diversity. We interpret this not as a failure, but as a computational demonstration of the “siloing” phenomenon that occurs when creative interactions are limited to a closed loop.